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python - Python OpenCV matchTemplate with Masking - 在所有位置找到匹配项

问题描述

问题： 我收到的结果matchTemplate表明我在每个位置都有匹配项，其值为1.0.

预期结果：我预计其中一个位置的results得分比其他位置高得多。

代码：

def template_match(filename=base_name,
                   img_folder=trn_imgs_path,
                   templates=['wet_install.png',
                              'wet_install_cleaned.png',
                              'wet_install_tag.png',
                              'wet_install_tag_cleaned.png'],
                   template_path=template_path,
                   threshold=0.8,
                   save_dir=save_dir):
    ''' 
    Perform template matching on an input image using a few templates.
    It draws bounding boxes on a copy of the original image. 

    Args:
        filename (str): name of the file with the .svg extension
        img_folder (str): path to folder containing the images
        templates (list): list of template filenames to match against
        template_path (str): path to folder containing the templates
        threshold (float): the threshold for a match from template matching
        save_dir (str): path to folder to save results
    '''
    print('Working on file: {}.png'.format(filename))

    # load the original BGR image
    img_rgb  = cv2.imread(img_folder + filename + '.png')[5143:5296, 15169:15368] # TODO(mtu): Don't keep these indices here!
    img_gray = cv2.cvtColor(img_rgb, cv2.COLOR_BGR2GRAY)
    img_gray = cv2.adaptiveThreshold(img_gray, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY, 115, 1)

    # loop over each template
    colors = [(0,0,255), (0,255,0), (255,255,0), (255,0,255)]
    for itemp in range(len(templates)):
        template_name = templates[itemp]
        print('Using Template: {}'.format(template_name))

        # load the template as grayscale and get its width and height
        template = cv2.imread(template_path + '{}'.format(template_name), 0)
        height, width = template.shape[:2]

        template  = cv2.adaptiveThreshold(template, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY, 115, 1)
        temp_mask = cv2.adaptiveThreshold(template, 1, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY_INV, 115, 1)

        # do template matching using grayscale image and find points above theshold
        results = cv2.matchTemplate(image=img_gray, templ=template, method=cv2.TM_CCORR_NORMED, mask=temp_mask)
        loc = np.where(results >= threshold)

        # draw rectangles on points above threshold on RGB image
        for pt in zip(*loc[::-1]):
            cv2.rectangle(img_rgb, pt, (pt[0] + width, pt[1] + height), colors[itemp%len(colors)], 5)

    # save the file with bounding boxes drawn on
    filename = save_dir + '{}_found.png'.format(filename) 
    print('Saving bounding boxes to: {}'.format(filename))
    cv2.imwrite(filename, img_rgb)

注释：

标签： pythonopencvmatchtemplate

解决方案

反转img_gray并template修复错误。

我使用的比较指标是cv2.TM_CCORR_NORMED. 这通过取二进制 numpy 数组具有 value的点积img_gray和其中的值来工作。templatetemp_mask1

在我的示例图像中，我想将黑色像素与 中的template黑色像素进行匹配img_gray，但是黑色的像素值为0. 因此，我想要检测的位置的点积很低。

通过反转img_gray，template我将白色像素template与img_gray. 由于白色具有像素值255，因此白色与白色、模板与图像的点积在我要检测的位置变高。

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